SLAM-CENTRIC FRAMEWORK FOR PRECISE AND PLATFORM-AGNOSTIC ROBOT-AIDED INFRASTRUCTURE INSPECTION

Robot-aided inspection has emerged as a promising solution for enhancing safety, efficiency, and objectivity in infrastructure assessment. However, existing approaches often suffer from inconsistent mapping accuracy, unreliable defect measurements, and platform-specific system designs that limit scalability. This study investigates whether a SLAM-centric (Simultaneous Localization and Mapping) framework can overcome these limitations and enable precise, repeatable, and platform-agnostic visual inspections across diverse infrastructure environments.

Integrated Lidar–Camera–Inertial SLAM Architecture

The proposed framework integrates lidar, camera, and inertial measurement unit (IMU) data within a unified SLAM pipeline to ensure robust localization and mapping under real-world conditions. Multi-sensor fusion enhances pose estimation accuracy and resilience to environmental challenges such as lighting variation, occlusions, and geometric complexity. By centering the inspection workflow around high-fidelity SLAM, the system establishes a reliable spatial reference for defect mapping and measurement, independent of the robotic platform employed.

Offline Trajectory Refinement and Inspection Map Generation

To further improve mapping precision, the framework incorporates offline trajectory refinement, reducing drift and cumulative localization errors commonly observed in real-time SLAM systems. Refined trajectories enable the generation of dense and geometrically consistent inspection maps. These maps serve as a unified spatial representation where inspection data can be consistently overlaid, facilitating repeatable assessments and longitudinal monitoring of infrastructure assets.

Automated Defect Extraction and 3D Ray-Tracing Projection

Visual defect detection is performed through image-based analysis, extracting cracks, spalls, and surface anomalies from captured imagery. A 3D ray-tracing technique projects detected defects into the unified inspection map, ensuring accurate spatial localization and dimensional quantification. This method allows precise measurement of defect size, orientation, and position within the 3D structure, significantly improving reliability compared to traditional qualitative or manual inspection methods.

Validation in Real-World Scenarios

Experimental validation in real-world environments demonstrates that the SLAM-centric framework produces accurate defect localization, consistent dimensional measurements, and high-density inspection maps. The platform-agnostic design ensures adaptability across different robotic systems, including ground vehicles, aerial drones, and climbing robots. The repeatability and robustness of the approach confirm its suitability for practical infrastructure inspection applications.

Implications for Long-Term Monitoring and Automation

By providing an end-to-end solution for robot-aided inspection, the proposed framework enables faster, safer, and more objective infrastructure assessments. The release of datasets and open software tools establishes a foundation for future research in long-term defect monitoring, inspection automation, and predictive maintenance. This SLAM-centric paradigm represents a significant step toward intelligent, data-driven infrastructure management systems.

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#InspectionAutomation

#SmartInfrastructure

#CivilEngineeringTechnology

#DroneInspection

#RoboticsInConstruction

#InfrastructureSafety

#AIforEngineering

#PredictiveMaintenance

#DigitalTwin

 

 

ADVANCING SUSTAINABLE WATER MANAGEMENT THROUGH CIVIL ENGINEERING INNOVATION

Sustainable water management has become a fundamental pillar of global environmental sustainability and resource conservation. Escalating water demand—driven by climate change, rapid urbanization, and population growth—has intensified pressure on existing water infrastructure systems. Civil engineering plays a decisive role in designing, upgrading, and managing water supply, wastewater, and stormwater systems to ensure long-term resilience and sustainability. This study provides a rigorous evaluation of how innovative engineering practices and emerging technologies are transforming water management toward more sustainable and equitable paradigms.

Sustainable Water Supply Systems and Technological Innovations

Modern water supply systems increasingly integrate advanced treatment technologies, smart monitoring networks, and decentralized distribution models to enhance efficiency and reduce resource losses. Innovations such as membrane filtration, smart metering, leak detection systems, and renewable energy integration are improving water quality and reducing operational footprints. These advancements not only enhance system reliability but also support water conservation strategies, energy efficiency, and long-term infrastructure resilience under climate variability.

Transformative Approaches in Wastewater Treatment

Wastewater treatment is evolving from a disposal-oriented process to a resource recovery platform. Advanced biological treatment processes, nutrient recovery technologies, and energy-positive treatment plants exemplify the transition toward circular water economies. Civil engineers are at the forefront of designing systems that recover water, energy, and valuable by-products, thereby reducing environmental discharge impacts while contributing to sustainable resource cycles. Such innovations significantly align wastewater management with broader sustainability objectives.

Sustainable Stormwater Management and Urban Resilience

Stormwater management has shifted from traditional drainage-based approaches to nature-based and low-impact development strategies. Green infrastructure solutions—such as permeable pavements, bioswales, retention ponds, and green roofs—mitigate flooding risks while enhancing groundwater recharge and urban biodiversity. These approaches strengthen climate adaptation capacity and reduce pollutant loads entering natural water bodies. The integration of ecological design principles within civil engineering practices is critical for achieving resilient and environmentally harmonious urban systems.

Barriers to Implementation and Strategic Solutions

Despite technological progress, widespread adoption of sustainable water management solutions faces financial, regulatory, technological, and societal challenges. High capital investment costs, outdated policies, limited technical expertise, and public acceptance issues can hinder implementation. This study identifies strategic pathways to overcome these barriers, including public–private partnerships, performance-based regulations, capacity-building initiatives, policy reform, and community engagement. Addressing these constraints is essential to accelerating the transition toward sustainable water governance frameworks.

Integrating Theory, Practice, and Policy for Future Sustainability

By combining theoretical sustainability frameworks with empirical case studies, this research underscores the necessity of interdisciplinary collaboration between academia, industry, and policymakers. Civil engineering innovation must be supported by evidence-based policy refinement and practical implementation strategies to ensure scalable impact. The study aims to inspire continued academic inquiry, technological development, and policy evolution, fostering a sustainable, efficient, and equitable water resource management paradigm that meets present and future global demands.

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#InfrastructureInnovation

#EnvironmentalEngineering

#SmartWater

#CircularEconomy

#SustainableCities

#ResilientInfrastructure

#WaterConservation

#EngineeringForSustainability

#PolicyAndInfrastructure

#ClimateAdaptation

#GlobalSustainability

 

FLUORINATED SILANE–MODIFIED POLYSILAZANE SUPERHYDROPHOBIC COATING WITH ENHANCED MECHANICAL AND ENVIRONMENTAL DURABILITY

Superhydrophobic coatings have gained increasing attention in structural engineering due to their ability to provide water repellency, anti-fouling performance, and surface protection against environmental degradation. However, achieving both high hydrophobicity and long-term durability remains a significant challenge. This study presents a novel fluorinated silane-modified organic polysilazane coating system designed to deliver superior water repellency, mechanical robustness, and environmental stability through a scalable and cost-effective fabrication approach.

Synthesis of Fluorinated Silane Coupling Agent

A fluorinated silane coupling agent was synthesized via hydrosilylation between 2-(perfluorohexyl)ethyl methacrylate and trimethoxysilane. The incorporation of perfluoroalkyl functional groups provides low surface energy, which is essential for achieving superhydrophobic behavior. This synthesized coupling agent was subsequently integrated into an organic polysilazane matrix, enhancing interfacial bonding and improving compatibility between the polymeric network and inorganic fillers.

Construction of Hierarchical Micro–Nano Surface Structure

To further amplify hydrophobic performance, micro–nano SiO₂ particles were introduced to form a hierarchical rough surface structure. The dual-scale roughness, combined with the low surface energy fluorinated silane component, enables the formation of a Cassie–Baxter wetting state. A simple spraying technique was employed to fabricate the coating, allowing practical application on diverse substrates including glass, metal, and concrete, thereby demonstrating strong versatility for structural applications.

Superhydrophobic Performance Evaluation

The optimized coating achieved a water contact angle (WCA) of 156.3° and a sliding angle of 5.6°, confirming its superhydrophobic characteristics. The high contact angle indicates excellent water repellency, while the low sliding angle reflects minimal adhesion between water droplets and the coated surface. These properties enable efficient water shedding and reduced moisture accumulation, which are critical for corrosion resistance and durability in structural environments.

Mechanical Robustness and Environmental Stability

Mechanical durability was assessed through repeated tape-peeling tests and sandpaper abrasion under controlled loading conditions. Even after 12 tape-peeling cycles or 360 cm abrasion under a 50 g load, the coating maintained a WCA above 150°, demonstrating strong adhesion and wear resistance under the tested conditions. Furthermore, exposure to alkaline solutions, saline environments, and UV radiation did not significantly compromise hydrophobic performance, indicating good chemical and photostability for outdoor structural use.

Self-Cleaning Capability and Structural Applications

The coating exhibited effective self-cleaning behavior against pigments and common liquids, allowing contaminants to be easily removed by rolling water droplets. This functionality reduces maintenance requirements and enhances surface longevity. Overall, the developed fluorinated silane–polysilazane superhydrophobic coating provides a simple, low-cost, and scalable strategy for protective applications in civil infrastructure, offering promising potential for waterproofing, anti-corrosion, and self-cleaning structural surfaces.

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#CorrosionProtection
#ConcreteProtection
#AdvancedMaterials
#CivilEngineeringMaterials
#HydrophobicCoatings
#SurfaceModification
#DurableCoatings
#SustainableConstruction
#Nanotechnology
#StructuralProtection
#EngineeringInnovation

NOVEL SELF-POWERED SENSOR (NSPS) FOR INTELLIGENT STRUCTURAL HEALTH MONITORING OF CIVIL INFRASTRUCTURE

 

Structural Health Monitoring (SHM) plays a critical role in ensuring the safety, durability, and serviceability of civil infrastructure such as bridges, buildings, and transportation systems. Conventional monitoring systems often depend on external power supplies and wired data transmission, limiting their scalability and long-term reliability. This study introduces a Novel Self-Powered Sensor (NSPS) specifically designed for civil structures, integrating self-energy harvesting, low-power wireless communication, and intelligent sensing capabilities. The proposed system addresses the limitations of traditional SHM by enabling sustainable, long-term, and autonomous infrastructure monitoring.

System Architecture and Core Technologies

The NSPS integrates three major technological components: environmental energy harvesting, ultra-low-power wireless data transmission, and intelligent sensing modules. The energy harvesting unit captures ambient environmental energy—such as vibration, solar, or thermal energy—and converts it into usable electrical power. The low-power wireless transmission system enables large-scale deployment across infrastructure networks without extensive cabling. Intelligent sensing algorithms process structural performance data efficiently, ensuring accurate detection of stress, deformation, and environmental variations under complex operational conditions.

Energy Harvesting Optimization and Power Management

A central innovation of the NSPS lies in its self-powered functionality. By fine-tuning the sensor design, optimal energy conversion efficiency is achieved from the harvesting unit, ensuring continuous operation even under variable environmental conditions. Advanced power management strategies regulate energy storage, consumption, and transmission cycles to maintain stable performance. This optimization enables the sensor to operate over extended periods without battery replacement, significantly reducing maintenance costs and enhancing the sustainability of monitoring systems.

Wireless Communication and Large-Scale Deployment

The NSPS employs large-scale, low-power wireless data transmission protocols that facilitate real-time structural performance monitoring across extensive infrastructure networks. This approach reduces installation complexity and allows flexible sensor placement in remote or hard-to-access areas. Compared to conventional wired systems, the wireless architecture improves coverage, scalability, and data accessibility, supporting integrated monitoring platforms for smart infrastructure management.

Bridge Case Study and Monitoring Strategy Development

To validate the practicality and effectiveness of the NSPS, a case study is conducted on an operational bridge structure. A monitoring scheme is developed based on the structural principles and load-bearing characteristics of the bridge. The sensor deployment strategy considers key stress zones, dynamic load responses, and environmental exposure conditions. Field testing demonstrates the system’s reliability in real-world scenarios, confirming its ability to continuously collect and transmit high-quality data while maintaining energy autonomy.

Sustainability, Performance Evaluation, and Future Applications

When compared to traditional structural monitoring techniques, the NSPS demonstrates significant improvements in sustainability, operational efficiency, and long-term reliability. The elimination of frequent battery replacement and reduced wiring requirements contribute to lower lifecycle costs and environmental impact. This innovative self-powered monitoring solution lays a strong foundation for future advancements in intelligent transportation systems and smart infrastructure. Further research may focus on multi-energy harvesting integration, AI-based damage prediction, and large-scale implementation across diverse civil engineering applications.

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#InfrastructureSafety
#SmartBridges
#StructuralMonitoring
#IoTSensors
#EngineeringResearch
#DigitalInfrastructure
#ResilientStructures
#TransportationEngineering
#SHMTechnology
#GreenEngineering

NANO-ENGINEERED NA-BENTONITE THIN FILM MEMBRANES FOR SUSTAINABLE WATERPROOFING OF CIVIL STRUCTURES

Waterproofing remains a critical challenge in the durability and service life of concrete infrastructure, particularly in environments exposed to moisture ingress and shrinkage-induced cracking. Conventional bentonite-based systems, while effective, often require high material consumption and may exhibit limitations in performance consistency. This study explores the development of nano-Na-bentonite derived from Egyptian bentonitic clay through solvothermal (NBS) and precipitation (NBP) synthesis routes. By leveraging nano-scale engineering, the research aims to enhance swelling behavior, hydrophobicity, and crack-sealing efficiency, ultimately offering a sustainable and high-performance alternative for waterproofing civil structures.

Material Preparation and Nano-Modification Techniques

The starting Egyptian bentonite was subjected to activation and purification processes to ensure the removal of impurities and optimization of montmorillonite content prior to nano-modification. Two synthesis approaches—solvothermal (NBS) and precipitation (NBP)—were employed to achieve nano-scale refinement. These processes facilitated controlled crystallite formation, yielding particle sizes of approximately 10 nm for NBS and 50 nm for NBP. The comparative evaluation of these methods provides insights into how synthesis pathways influence structural refinement, morphology, and functional performance in waterproofing membranes.

Structural, Chemical, and Thermal Characterization

Comprehensive characterization techniques were utilized to investigate the physicochemical properties of NBS and NBP. X-ray diffraction (XRD) confirmed the preservation of montmorillonite as the dominant mineral phase, while X-ray fluorescence (XRF) validated the retention of essential chemical constituents. Fourier transform infrared spectroscopy (FTIR) identified characteristic functional groups associated with clay minerals, and TGA/DTA analyses demonstrated thermal stability and moisture-related mass changes. The nano-size effect was evident in the enhanced swelling capacities of 16.3 g/mm for NBS and 12 g/mm for NBP, significantly exceeding that of purified bentonite, thereby substantiating the influence of nano-engineering on performance enhancement.

Morphological Features and Swelling Behavior

Scanning electron microscopy (SEM) revealed that both NBS and NBP exhibit continuous wire-like morphologies with spherical and platelet nanostructures, contributing to improved surface area and interaction with water molecules. Micro-scale swelling measurements demonstrated remarkable volumetric expansion upon hydration, facilitating the formation of an impermeable gel layer. The reduced crystallite size in NBS particularly enhanced swelling kinetics, while NBP exhibited more uniform crack-sealing behavior. These morphological and swelling characteristics play a pivotal role in preventing water penetration and improving long-term waterproofing efficiency.

Hydrophobicity and Water Resistance Performance

Prototype thin film membranes fabricated from NBS and NBP were evaluated using water droplet contact angle analysis. Both membranes demonstrated excellent hydrophobic behavior, with contact angles ranging from 90° to 105° and low spreading coefficients between –81 and –86 mN/m. Notably, the membranes withstood 250–300 water droplets (1–1.2 mm height) without penetration, forming a stable protective gel layer on the surface. These findings confirm the enhanced water-repellent characteristics imparted by nano-scale modification and underscore their suitability for high-performance waterproofing systems.

Crack Morphology Analysis and Application Potential

Post-shrinkage crack morphology was assessed using USB digital microscopy to determine the crack-sealing efficiency of the membranes. The NBP membrane exhibited significantly reduced crack widths (0.09–0.18 mm) compared to NBS (0.22–0.58 mm), indicating superior crack mitigation capability. This suggests that precipitation-synthesized nano-bentonite may offer enhanced structural compatibility and sealing efficiency in concrete substrates. Overall, the performance of NBS and NBP thin film membranes highlights their potential as sustainable, low-consumption alternatives to conventional bentonite systems. Future investigations should focus on long-term durability, environmental exposure testing, and large-scale field implementation to validate their practical applicability in civil infrastructure.

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#ThinFilmMembrane

#HydrophobicCoatings

#SwellingClay

#StructuralEngineering

#BuildingMaterials

#InfrastructureInnovation

#MaterialCharacterization

#GreenConstruction

#AdvancedMaterials

#CrackSealingTechnology

#GeotechnicalEngineering

#SustainableInfrastructure

Neural Backstepping Output-Constrained Control for Flexible Civil Aircraft Overload Tracking

Ride quality and flight safety are critical performance indicators for flexible civil aircraft, particularly under atmospheric disturbances such as gusts and turbulence. Normal overload tracking must satisfy stringent comfort and safety standards defined by ISO 2631-1 and MIL-F-9490D. This paper proposes a neural backstepping output-constrained control strategy to ensure accurate overload tracking while strictly respecting ride quality constraints.

Normal Overload Constraints and Control Objectives

The normal overload constraint arises from the need to limit excessive accelerations that degrade passenger comfort and structural integrity. To explicitly address these constraints, the control design incorporates an integral barrier Lyapunov function (IBLF), which guarantees that the overload response remains within a predefined safe interval throughout system operation.

Neural Backstepping Control Framework

A backstepping-based control architecture is developed for the flexible aircraft model, enabling systematic handling of nonlinear dynamics and output constraints. The IBLF-based control laws ensure constraint satisfaction while maintaining stable overload tracking performance, even in the presence of flexible-body effects inherent in civil aircraft structures.

Handling Model Uncertainty and External Disturbances

To address modeling uncertainties and unknown external disturbances, neural networks are embedded within the control framework to approximate uncertain nonlinearities. In parallel, a disturbance observer is employed to estimate and compensate for external disturbances. A composite learning strategy is further introduced to enhance neural network learning efficiency and convergence accuracy.

Stability and Constraint Satisfaction Analysis

Lyapunov stability theory is used to rigorously prove the uniformly ultimate boundedness of all closed-loop system signals. The analysis also confirms that the normal overload remains strictly within the prescribed bounds imposed by ride quality requirements, ensuring both theoretical soundness and practical reliability of the proposed controller.

Simulation and Hardware-in-the-Loop Validation

Numerical simulations and hardware-in-the-loop (HIL) experiments were conducted under typical discrete gusts and atmospheric turbulence conditions. Results demonstrate that the proposed controller significantly improves ride quality, effectively suppresses overload fluctuations, and consistently maintains the normal overload within the predefined safety interval, confirming its suitability for real-world civil aircraft applications.

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#AerospaceEngineering

#FlexibleAircraft

#NonlinearControl

#LyapunovStability

#DisturbanceObserver

#CompositeLearning

#HILSimulation

#AircraftDynamics

#ControlSystems

#AviationSafety

#IntelligentControl

#EngineeringResearch

#AdvancedFlightControl

 

Intelligent Infrastructure Crack Detection Using MSEDBO-Optimized Deep Learning

 

Infrastructure surface crack detection is a vital task in structural health monitoring, directly influencing the safety, durability, and serviceability of civil engineering assets. Although deep learning methods have achieved notable success in automated crack detection, their performance is often constrained by inefficient hyperparameter tuning, susceptibility to local optima, and suboptimal feature extraction. This study addresses these limitations by proposing an intelligent optimization-driven crack detection framework.

Limitations of Conventional Deep Learning-Based Crack Detection

Traditional deep learning models rely heavily on manual or heuristic-based hyperparameter selection, which can lead to unstable training outcomes and reduced generalization performance. Moreover, commonly used optimization techniques may become trapped in local optima, resulting in inaccurate crack localization and increased false positive rates, particularly when dealing with complex backgrounds and diverse infrastructure materials.

Multi-Strategy Enhanced Dung Beetle Optimizer (MSEDBO)

The proposed framework integrates a Multi-Strategy Enhanced Dung Beetle Optimizer (MSEDBO) to systematically optimize critical parameters within the crack detection pipeline. MSEDBO incorporates Latin Hypercube Sampling with elite population initialization, an improved sigmoid-based nonlinear control factor, sine–cosine algorithm integration, and multi-population mutation strategies. These enhancements collectively strengthen global exploration and local exploitation capabilities.

Integration with Deep Learning Models

By embedding MSEDBO into deep learning-based crack detection models, the framework enables adaptive optimization of network parameters and feature extraction processes. This synergy improves convergence behavior, enhances robustness against local optima, and ensures efficient learning across varying crack patterns and surface conditions in civil infrastructure.

Experimental Validation and Benchmark Datasets

The proposed approach was validated using multiple benchmark datasets, including CrackTree200, CFD, GAPs, and SDNET2018, covering a wide range of materials such as concrete pavements, asphalt roads, and bridge surfaces. Comparative experiments demonstrate that the MSEDBO-optimized framework consistently outperforms conventional optimization algorithms and baseline deep learning models.

Performance Gains and Practical Implications

Results show significant improvements, including an 8.7% increase in detection accuracy, a 12.3% improvement in precision, and a 15.6% reduction in false positive rates. The framework maintains computational efficiency while effectively avoiding local optima, making it well suited for real-world deployment. This research advances intelligent infrastructure monitoring by providing a robust optimization strategy to enhance the reliability and accuracy of automated crack detection systems.

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#AIinCivilEngineering

#AutomatedInspection

#ConcreteCracks

#RoadSurfaceMonitoring

#BridgeInspection

#MachineLearning

#EngineeringOptimization

#DigitalInfrastructure

#SustainableInfrastructure

#CivilEngineeringResearch